placestats

Hospital beds vs. Tertiary enrollment

Fades after adjusting for income

Across 160 countries, Hospital beds and Tertiary enrollment rise and fall together at r = +0.52 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.23, which could easily be chance (q = 0.059, allowing for the many pairs tested, and allowing for countries in the same region being alike). So the first figure mostly shows richer countries differing from poorer ones, not a link between these two things. Caution: direction is not consistent across regions.

Comparing countries across the middle half of hospital beds, 1.19 to 3.82 per 1,000 people, tertiary enrollment is better by 19.4% gross at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 7.01% gross.

Link before adjusting (r)+0.519p = 2.1e-12 · 160 countries
After adjusting for national income+0.232p = 0.003 · likely range [+0.08, +0.37]
p, allowing for regions0.007standard error 0.058 · 7 regions
Check for luck (q)0.059across 12,497 pairs

How to read these.r runs from −1 (one goes up as the other goes down) to +1 (they rise together), and 0 means no link. Adjusting for national income means comparing countries with the same GDP per person, so differences in wealth can’t explain the link. Income is used in percentage terms, since an extra $1,000 means more to a poor country than to a rich one. p is how likely a link this strong would be if there were really none. Below 0.05 means it is unlikely to be chance. Very small values are written in short form: 1.1e-32 means a decimal point followed by 31 zeros and then 11, so very close to zero. The likely range is where the true figure probably lies, and a range that crosses zero means the link could easily be chance. Countries in the same region tend to resemble each other, so the third figure allows for that, which gives a fairer p. The standard error is the typical size of the error in r. Finally, testing this many pairs means some will look strong by luck alone. A q below 0.05 means this link is unlikely to be one of those.

Hospital beds · per 1,000 peoplelink with income +0.63 · high
Tertiary enrollment · % grosslink with income +0.75 · high

Both of these rise and fall closely with national income, so a strong link between them before adjusting was close to certain. Read the adjusted figure, not the first one.

“Link with income” is how closely each measure tracks GDP per person across countries, from −1 to +1.

Every country as a dot

Hospital beds · per 1,000 people →Tertiary enrollment · % gross →0.1512.82.57%165%Afghanistan: 0.35 per 1,000 people, 10.9% grossAngola: 0.75 per 1,000 people, 9.95% grossAlbania: 2.9 per 1,000 people, 84.1% grossUnited Arab Emirates: 1.87 per 1,000 people, 63.7% grossArgentina: 3.36 per 1,000 people, 108% grossArmenia: 4.24 per 1,000 people, 52.8% grossAntigua and Barbuda: 3.42 per 1,000 people, 25.1% grossAustralia: 3.82 per 1,000 people, 108% grossAustria: 6.7 per 1,000 people, 92.6% grossAzerbaijan: 3.68 per 1,000 people, 41.3% grossBurundi: 0.69 per 1,000 people, 6.38% grossBelgium: 5.42 per 1,000 people, 82.1% grossBenin: 0.43 per 1,000 people, 10.2% grossBurkina Faso: 0.2 per 1,000 people, 10% grossBangladesh: 0.92 per 1,000 people, 23.7% grossBulgaria: 8.2 per 1,000 people, 69.5% grossBahrain: 1.84 per 1,000 people, 58.4% grossBosnia and Herzegovina: 2.35 per 1,000 people, 45.5% grossBelarus: 9.77 per 1,000 people, 70.1% grossBelize: 1 per 1,000 people, 24.8% grossBrazil: 2.52 per 1,000 people, 69.7% grossBarbados: 6.4 per 1,000 people, 50.8% grossBrunei Darussalam: 3.58 per 1,000 people, 36.4% grossBhutan: 2.16 per 1,000 people, 13% grossBotswana: 2.36 per 1,000 people, 21.4% grossCentral African Republic: 1 per 1,000 people, 2.78% grossCanada: 2.54 per 1,000 people, 76.3% grossSwitzerland: 4.38 per 1,000 people, 73.5% grossChile: 1.94 per 1,000 people, 110% grossChina: 5.63 per 1,000 people, 76.9% grossCameroon: 2.65 per 1,000 people, 16.7% grossColombia: 1.7 per 1,000 people, 59.3% grossComoros: 2.16 per 1,000 people, 18.8% grossCabo Verde: 2.21 per 1,000 people, 24.3% grossCosta Rica: 1.14 per 1,000 people, 55% grossCyprus: 2.15 per 1,000 people, 121% grossCzechia: 6.54 per 1,000 people, 66.7% grossGermany: 7.55 per 1,000 people, 76.7% grossDjibouti: 1.4 per 1,000 people, 5.16% grossDenmark: 2.43 per 1,000 people, 79% grossDominican Republic: 0.75 per 1,000 people, 57.7% grossAlgeria: 1.61 per 1,000 people, 54.4% grossEcuador: 1.32 per 1,000 people, 67.9% grossEgypt, Arab Rep.: 1.06 per 1,000 people, 38% grossSpain: 2.91 per 1,000 people, 93.8% grossEstonia: 4.13 per 1,000 people, 65.7% grossEthiopia: 0.33 per 1,000 people, 5.92% grossFinland: 2.61 per 1,000 people, 110% grossFiji: 1.89 per 1,000 people, 75% grossFrance: 5.65 per 1,000 people, 71.5% grossUnited Kingdom: 2.42 per 1,000 people, 80.4% grossGeorgia: 4.04 per 1,000 people, 89.5% grossGhana: 0.69 per 1,000 people, 21.3% grossGuinea: 0.3 per 1,000 people, 10.9% grossGambia, The: 1.11 per 1,000 people, 13.5% grossGreece: 4.27 per 1,000 people, 165% grossGrenada: 3.01 per 1,000 people, 100% grossGuatemala: 0.45 per 1,000 people, 27.3% grossGuyana: 3.24 per 1,000 people, 26.4% grossHonduras: 0.53 per 1,000 people, 23.3% grossCroatia: 5.6 per 1,000 people, 81.2% grossHungary: 6.63 per 1,000 people, 60.5% grossIndonesia: 1.37 per 1,000 people, 44.9% grossIndia: 1.59 per 1,000 people, 34.4% grossIreland: 2.96 per 1,000 people, 77.6% grossIran, Islamic Rep.: 1.85 per 1,000 people, 58.7% grossIceland: 2.83 per 1,000 people, 76.5% grossIsrael: 3.14 per 1,000 people, 56.3% grossItaly: 3.06 per 1,000 people, 75.9% grossJamaica: 1.65 per 1,000 people, 26.2% grossJordan: 1.41 per 1,000 people, 35.9% grossJapan: 12.6 per 1,000 people, 64.5% grossKazakhstan: 6.54 per 1,000 people, 52.9% grossKenya: 1.33 per 1,000 people, 10.4% grossKyrgyz Republic: 3.73 per 1,000 people, 53.9% grossCambodia: 0.73 per 1,000 people, 21.4% grossKiribati: 1.86 per 1,000 people, 4.96% grossSt. Kitts and Nevis: 3.98 per 1,000 people, 93.9% grossKorea, Rep.: 12.8 per 1,000 people, 112% grossKuwait: 2.33 per 1,000 people, 51.9% grossLao PDR: 1.71 per 1,000 people, 14.9% grossLebanon: 2.73 per 1,000 people, 54.4% grossLiberia: 1.59 per 1,000 people, 10.8% grossSt. Lucia: 1.09 per 1,000 people, 15% grossSri Lanka: 3.93 per 1,000 people, 20.7% grossLithuania: 5.71 per 1,000 people, 75.6% grossLuxembourg: 3.95 per 1,000 people, 22.4% grossLatvia: 4.95 per 1,000 people, 82.9% grossMorocco: 0.73 per 1,000 people, 48.2% grossMoldova: 5.54 per 1,000 people, 74.7% grossMadagascar: 0.32 per 1,000 people, 6.15% grossMaldives: 4.56 per 1,000 people, 48.5% grossMexico: 1.01 per 1,000 people, 48.2% grossMarshall Islands: 2.8 per 1,000 people, 37.7% grossNorth Macedonia: 4.74 per 1,000 people, 55% grossMali: 0.25 per 1,000 people, 5.13% grossMalta: 4.09 per 1,000 people, 80.5% grossMyanmar: 1.07 per 1,000 people, 20.4% grossMontenegro: 3.82 per 1,000 people, 55.2% grossMongolia: 8.58 per 1,000 people, 69% grossMozambique: 0.74 per 1,000 people, 7.46% grossMauritius: 3.8 per 1,000 people, 45.8% grossMalawi: 1.3 per 1,000 people, 2.71% grossMalaysia: 1.96 per 1,000 people, 38.5% grossNiger: 0.28 per 1,000 people, 4.15% grossNicaragua: 0.97 per 1,000 people, 30.4% grossNetherlands: 2.42 per 1,000 people, 86.6% grossNorway: 3.3 per 1,000 people, 95.5% grossNepal: 0.54 per 1,000 people, 21.3% grossNew Zealand: 2.51 per 1,000 people, 77.2% grossOman: 0.99 per 1,000 people, 42.2% grossPakistan: 0.63 per 1,000 people, 10.9% grossPanama: 1.95 per 1,000 people, 53.7% grossPeru: 1.56 per 1,000 people, 71.2% grossPhilippines: 0.97 per 1,000 people, 47.4% grossPalau: 4.34 per 1,000 people, 71.8% grossPoland: 6.04 per 1,000 people, 80.9% grossPortugal: 3.48 per 1,000 people, 77.7% grossParaguay: 1.04 per 1,000 people, 37.6% grossWest Bank and Gaza: 1.3 per 1,000 people, 44.5% grossQatar: 1.25 per 1,000 people, 43% grossRomania: 7.23 per 1,000 people, 55.2% grossRussian Federation: 6.81 per 1,000 people, 60.4% grossRwanda: 0.75 per 1,000 people, 9.41% grossSaudi Arabia: 2.41 per 1,000 people, 83.9% grossSudan: 0.66 per 1,000 people, 15.9% grossSenegal: 0.71 per 1,000 people, 15.3% grossSingapore: 2.81 per 1,000 people, 97.3% grossEl Salvador: 1.21 per 1,000 people, 32.8% grossSan Marino: 3.35 per 1,000 people, 57.5% grossSerbia: 5.78 per 1,000 people, 70.6% grossSao Tome and Principe: 2.91 per 1,000 people, 12.8% grossSuriname: 2.87 per 1,000 people, 2.57% grossSlovak Republic: 5.57 per 1,000 people, 54.5% grossSlovenia: 4.11 per 1,000 people, 80.1% grossSweden: 1.9 per 1,000 people, 84% grossEswatini: 2.02 per 1,000 people, 6.44% grossSeychelles: 2.63 per 1,000 people, 14.5% grossSyrian Arab Republic: 1.43 per 1,000 people, 43.8% grossChad: 0.15 per 1,000 people, 4.4% grossTogo: 0.33 per 1,000 people, 14.9% grossThailand: 2.39 per 1,000 people, 49.1% grossTajikistan: 4.04 per 1,000 people, 35.6% grossTurkmenistan: 3.6 per 1,000 people, 20.4% grossTimor-Leste: 0.38 per 1,000 people, 31% grossTonga: 2.48 per 1,000 people, 42.9% grossTrinidad and Tobago: 2.37 per 1,000 people, 23.8% grossTunisia: 1.82 per 1,000 people, 38.1% grossTurkiye: 3.05 per 1,000 people, 106% grossTanzania: 0.84 per 1,000 people, 4.02% grossUganda: 0.5 per 1,000 people, 4.62% grossUkraine: 6.14 per 1,000 people, 87.2% grossUruguay: 2.44 per 1,000 people, 80% grossUnited States: 2.68 per 1,000 people, 79.4% grossUzbekistan: 4.89 per 1,000 people, 59.8% grossSt. Vincent and the Grenadines: 3.21 per 1,000 people, 41.5% grossViet Nam: 2.52 per 1,000 people, 37.6% grossSouth Africa: 2.25 per 1,000 people, 23.5% grossZambia: 2 per 1,000 people, 3.96% grossZimbabwe: 1.95 per 1,000 people, 7.75% grossBLRCHLCYPGRCJPNKORMLINERTCD

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5192.1e-12160
By rank (Spearman), not adjusted+0.6313.9e-19160
Standard (Pearson), adjusted for national income+0.2320.003160
By rank (Spearman), adjusted for national income+0.2730.000160

The rank version uses only each country’s place in line, from lowest to highest, so a few extreme countries count for less. The adjusted and unadjusted versions use the same 160 countries: those that report both measures and GDP per person. So the gap between them comes from the adjustment, not from a change in which countries are counted. Drop in the size of r after adjusting: +0.287 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.195 (without KOR) to +0.257 (without JPN). The bar above shows that range on a scale from −1 to +1, with the solid line at the full result. The single country that moves r most: KOR (-0.037).

Fragile: this link may not hold up.
  • direction is not consistent across regions

Within each region

Countries are not separate, unrelated cases, and neighbors share a lot. A link that holds inside each region is far more believable than one that shows up only when comparing regions.

nr
East Asia & Pacific22+0.35
Europe & Central Asia49-0.13
Latin America & Caribbean27+0.17
Middle East, North Africa, Afghanistan & Pakistan20+0.56
Sub-Saharan Africa34+0.32

By income third

If the link shows up only in the richest third of countries, it is a feature of rich countries, not a general pattern.

nr
Poorest third54+0.29
Middle third53+0.26
Richest third53+0.06

By largest religious group

Countries grouped by a label they share, such as main religion, rather than by a number. Groups of fewer than twelve countries are not shown. These groups differ in income, region and history all at once. So a gap between them is a place to start asking questions, not an answer.

nr
Christians94+0.16
Muslims41+0.30

By last european coloniser

Countries grouped by a label they share, such as main religion, rather than by a number. Groups of fewer than twelve countries are not shown. These groups differ in income, region and history all at once. So a gap between them is a place to start asking questions, not an answer.

nr
France20+0.33
Never a European colony21+0.30
Spain17+0.68
United Kingdom49+0.16

Without outliers (countries far outside the middle half of countries on either measure, by more than one and a half times the width of that middle half): r = +0.203 after dropping 6 countries.

Two series, one country

Both measures over the years for one country you pick. The two charts are stacked so they share only the years along the bottom.

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Over time, within countries

Everything above compares countries with each other at one moment. This section compares each country only with itself over 34years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

Within countries over time, there is no link that is clearly more than chance (r = +0.066, comparing each country only with itself over the years, and each year only with the same year elsewhere). Whatever the cross-country picture shows, countries whose hospital beds moved did not tend to see their tertiary enrollment move with it.

Across countries (adjusted)+0.232for comparison
Within countries-0.247each country vs itself
…and vs the same year elsewhere+0.066q = 0.658 · 131 countries
Year-on-year changes+0.041q = 0.239
The direction flips. Comparing each country only with itself gives -0.25. Also taking out what happened to the whole world each year gives +0.07. So the first figure was picking up a trend both measures share with worldwide progress, not a link between them.

2,406 country-years (one country in one year), 131 countries, 1990–2023, about 18 years each on average. Share of the variation that happens within countries over time: 14% for Hospital beds, 21% for Tertiary enrollment. The margins of error allow for each country appearing many times, and for each year’s value being close to the year before.

Year-to-year changes are a cautious test, not a final answer. Looking only at changes from one year to the next removes each country’s own trends. But it also makes measurement mistakes louder, which pulls the results toward zero. A near-zero result here is weak evidence of no link, not proof.

With one measure years behind the other

0.10−0.1-100+10years Tertiary enrollment trails Hospital beds →lag -10: r = -0.043 (n = 1,590)lag -9: r = -0.021 (n = 1,663)lag -8: r = 0.002 (n = 1,736)lag -7: r = 0.032 (n = 1,810)lag -6: r = 0.059 (n = 1,915)lag -5: r = 0.056 (n = 1,996)lag -4: r = 0.051 (n = 2,055)lag -3: r = 0.067 (n = 2,154)lag -2: r = 0.070 (n = 2,236)lag -1: r = 0.075 (n = 2,308)lag +0: r = 0.066 (n = 2,406)lag +1: r = 0.055 (n = 2,425)lag +2: r = 0.046 (n = 2,388)lag +3: r = 0.038 (n = 2,340)lag +4: r = 0.039 (n = 2,266)lag +5: r = 0.036 (n = 2,210)lag +6: r = 0.034 (n = 2,151)lag +7: r = 0.044 (n = 2,075)lag +8: r = 0.050 (n = 2,008)lag +9: r = 0.068 (n = 1,972)lag +10: r = 0.093 (n = 1,910)

Same year (lag 0): +0.066. A peak away from zero does not show which comes first. These series change slowly — this year’s value is close to last year’s. That makes the curve smooth and nearly symmetric, whatever produced the data. Even a sharp peak at one lag (a gap of some years) would not prove that one measure changes the other.

This curve peaks at the edge of the window (lag +10), so it is still drifting where the search stops. That pattern usually reflects a slow trend left over in the data, not a link with real timing. The peak value should not be read as the “true” link at the right lag.

The data


per 1,000 people
% gross		Years
Korea, Rep. KOR12.8112%2022, 2024
Japan JPN12.664.5%2022, 2023
Belarus BLR9.7770.1%2023, 2024
Mongolia MNG8.5869%2023, 2024
Bulgaria BGR8.269.5%2023, 2024
Germany DEU7.5576.7%2023, 2024
Romania ROU7.2355.2%2022, 2023
Russian Federation RUS6.8160.4%2023, 2024
Austria AUT6.792.6%2022, 2024
Hungary HUN6.6360.5%2022, 2024
Kazakhstan KAZ6.5452.9%2020, 2025
Czechia CZE6.5466.7%2022, 2023
Barbados BRB6.450.8%2023, 2025
Ukraine UKR6.1487.2%2022, 2024
Poland POL6.0480.9%2022, 2024

World Bank, World Development Indicators
World Bank, World Development Indicators

Sources

Hospital beds

Hospital beds (per 1,000 people)

World Bank · CC BY 4.0 · updated quarterly

Tertiary enrollment

School enrollment, tertiary (% gross)

UNESCO Institute for Statistics · CC BY 4.0 · updated quarterly

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2025. To adjust for income, each measure is first predicted from GDP per person (in percentage terms, on a log scale) with a straight-line fit. Only what is left over is then compared. This is called a partial correlation. The line is drawn the standard way (ordinary least squares), which picks the line that comes closest to all the countries at once. The error margins allow for countries in the same region resembling each other, using 7 World Bank regions. The q-value corrects for testing all 12,497 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Hospital beds and Tertiary enrollment

This page shows that two measures go together. It does not show that Hospital beds changes Tertiary enrollment, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.

More links with Hospital beds or Tertiary enrollment